A Graph Machine Learning approach to Automatic Dementia Detection

Edoardo Stoppa; Guido Walter Di Donato; Isabella Poles; Eleonora D'Arnese; Natalie Parde; Marco D Santambrogio

Abstract

Dementia is a term used to refer to a wide range of diseases that cause a decline in cognitive abilities. This decline is severe enough to impair daily life and it is extremely complex to diagnose in its early stages. In recent years multiple Natural Language Processing solutions have been proposed to automatically detect dementia. One of the main approaches to this problem is based on extracting manually engineered features from a set of patients' conversations and feeding them to traditional Machine Learning models. These features can be divided into very different groups, and we can define specific relations that connect one feature to the other. Thus, we introduce a new way to approach the problem by organizing all the extracted features in a graph structure and using Graph Machine Learning to detect dementia. We validate our method using a well-established score regression task and a newly proposed multi-class classification task. This new task is based on the mapping between the Mini-Mental State Examination score and multiple dementia severity levels. Compared to traditional Machine Learning, our Graph Machine learning technique achieves a relative increase in performance between 2.9% and 8% for the regression task, and between 4.4% and 7.9% for the classification task.

Keywords: Graph Machine Learning; Dementia Detection; Natural Language Processing

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